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We propose the attentive prototype for learning representative class features in the presence of label noise by leveraging self-attention through a transformer block and perform source-free unsupervised domain adaptation of 3D object detection networks that mitigates the effect of label noise during self training by filtering incorrect annotations.

We demonstrate our method on two recent object detectors, SECOND-iou [1]}, and PointRCNN [2]} for six domain shift scenarios and outperform recent domain adaptation works.

